It is clear from fig::objrgb that object SLAM with deep learning object detection has two major challenges.
First, there are multiple instances of the object class, such as “chair” in fig::objrgb.
Without correct data association, it is hard to distinguish different object instances. Standard pose-graph SLAM algorithms can only optimize poses with exact data association, such as g2o[1]}, isam[2]}, gtsam[3]}.
The second challenge is high false positive rates.
As the chair detected in fig::objrgb, deep learning algorithms report objects now and then when there are actually none.
Blindly using these unfiltered detections in standard SLAM algorithms will lead to “non-exist" nodes and cause loop closure failures.
